發(fā)布時(shí)間：2018-02-04 08:40

  本文關(guān)鍵詞： 復(fù)雜系統(tǒng) 排隊(duì)論模型 動(dòng)力特征分析 出行行為　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),隨著社會(huì)的發(fā)展和人類對(duì)社會(huì)服務(wù)領(lǐng)域要求的提高,居民出行行為的特征與方式逐漸成為了一個(gè)熱門的課題,掌握更多的居民出行行為的信息就能夠更好的服務(wù)于人類的生活。長(zhǎng)久以來(lái),由于居民出行行為在其模型建立和應(yīng)用特征的分析上具有雙重價(jià)值,吸引了許多領(lǐng)域?qū)W者的共同關(guān)注。本文研究的主要內(nèi)容是:在居民出行行為的動(dòng)力學(xué)模型方面,首先針對(duì)個(gè)體和群體模型進(jìn)行研究,之后分別利用模擬數(shù)據(jù)和實(shí)證數(shù)據(jù)驗(yàn)證其準(zhǔn)確性,最后再產(chǎn)生替代數(shù)據(jù)并驗(yàn)證其準(zhǔn)確性;在居民出行行為的特征分析方面,將浮動(dòng)車數(shù)據(jù)可視化到地圖,融合聚類分析算法,挖掘出深圳市居民上下車的興趣點(diǎn)。此外,我們以此為基礎(chǔ)針對(duì)深圳市出租車的載客模式進(jìn)行了特征提取,挖掘典型的載客特征。論文的主要內(nèi)容包括下面幾個(gè)部分:在模型研究方面,本文對(duì)個(gè)體和群體出行分別進(jìn)行研究。針對(duì)個(gè)體出行,從出租車和乘客兩者角度出發(fā),以載客的時(shí)間間隔為切入點(diǎn),分別推導(dǎo)有關(guān)于出租車載客次數(shù)和乘客打車次數(shù)的模型。針對(duì)群體出行,基于對(duì)排隊(duì)論模型的理解,對(duì)原有的人類動(dòng)力學(xué)模型進(jìn)行推廣改進(jìn),推廣改進(jìn)模型后分別應(yīng)用模擬數(shù)據(jù)以及實(shí)際的數(shù)據(jù)對(duì)其進(jìn)行驗(yàn)證。該模型分別選取了具有不同特性的個(gè)體去推導(dǎo)群體的特性,但群體的結(jié)果卻都服從冪律分布,從而能夠說(shuō)明群體出行的特征并不是個(gè)體出行特征的疊加。在驗(yàn)證改進(jìn)模型的準(zhǔn)確性后,基于改進(jìn)模型產(chǎn)生居民出行行為的替代數(shù)據(jù),并驗(yàn)證其準(zhǔn)確性,從而豐富了用來(lái)研究人類行為動(dòng)力學(xué)的數(shù)據(jù)。在特征分析方面,選擇合適的地圖匹配算法,完成浮動(dòng)車數(shù)據(jù)的地圖可視化。之后,基于對(duì)浮動(dòng)車數(shù)據(jù)的統(tǒng)計(jì)分析,使用直觀形象的地圖可視化工具完成居民出行行為的興趣點(diǎn)挖掘,生成熱應(yīng)力圖,并對(duì)居民出行行為進(jìn)行特征分析。為了驗(yàn)證使用上述地圖可視化工具挖掘居民出行興趣點(diǎn)的準(zhǔn)確性,使用更加精確的聚類分析算法對(duì)浮動(dòng)車數(shù)據(jù)進(jìn)行處理。本文通過(guò)比較不同的聚類分析算法,選擇凝聚式層次聚類分析算法對(duì)浮動(dòng)車數(shù)據(jù)進(jìn)行挖掘,并結(jié)合更加精確的電子地圖進(jìn)行地圖可視化,通過(guò)與熱應(yīng)力圖所呈現(xiàn)出來(lái)的興趣點(diǎn)比對(duì),驗(yàn)證其準(zhǔn)確性�；隍�(yàn)證準(zhǔn)確的熱應(yīng)力圖為基礎(chǔ),從出租車的運(yùn)行區(qū)域出發(fā)對(duì)深圳市出租車的載客模式進(jìn)行歸納總結(jié),我們認(rèn)為居民在出行的空間位置上近似服從于正態(tài)分布,由此本文完成了對(duì)居民出行行為的時(shí)間間隔和空間跨度的模型建立和特征分析。
[Abstract]:In recent years, with the development of society and the improvement of human requirements in the field of social services, the characteristics and patterns of residents' travel behavior have gradually become a hot topic. Having more information on the travel behavior of residents can better serve the human life. For a long time, because of the resident travel behavior in its model building and application of the analysis of the characteristics of the dual value. It has attracted the common attention of many scholars. The main content of this paper is: in the dynamic model of resident travel behavior, the individual and group models are studied first. After that, the simulation data and empirical data are used to verify its accuracy, and then substitute data are generated to verify its accuracy. In the aspect of characteristic analysis of residents' travel behavior, the floating vehicle data is visualized to the map, and the clustering analysis algorithm is combined to find out the points of interest of the residents in Shenzhen. Based on this, we extract the features of the passenger mode of Shenzhen taxi, mining the typical passenger characteristics. The main content of this paper includes the following parts: in the research of the model. In this paper, the individual and group travel are studied separately. For individual travel, from the perspective of taxi and passenger, take the time interval of passengers as the breakthrough point. For group travel, based on the understanding of queuing theory model, the existing human dynamics model is extended and improved. The improved model is validated by simulation data and actual data. The model selects individuals with different characteristics to deduce the characteristics of the population, but the results of the population are distributed according to the power law. It can show that the characteristics of group travel is not the superposition of individual travel characteristics. After verifying the accuracy of the improved model, the improved model can generate the replacement data of the residents' travel behavior, and verify its accuracy. It enriches the data used to study the dynamics of human behavior. In the aspect of feature analysis, the appropriate map matching algorithm is selected to complete the map visualization of floating vehicle data. Based on the statistical analysis of floating vehicle data, the visual map visualization tool is used to mine the points of interest of residents' travel behavior and generate the thermal stress map. In order to verify the accuracy of using the above map visualization tools to mine the residents' travel interest points. Using more accurate clustering analysis algorithm to deal with floating vehicle data. By comparing different clustering analysis algorithms, this paper selects the condensed hierarchical clustering analysis algorithm to mine floating vehicle data. Combined with the more accurate electronic map to visualize the map, the accuracy of the map is verified by comparing it with the points of interest shown in the thermal stress map, which is based on the verification of the accurate thermal stress map. Starting from the operating area of taxis in Shenzhen, we summarize the passenger carrying mode of taxis in Shenzhen. We think that the residents in the travel space position is similar to the normal distribution. In this paper, the time interval and spatial span of residents' travel behavior are modeled and analyzed.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491

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